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Merge pull request numpy#26770 from melissawm/update-nep44
DOC, NEP: Update NEP44
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doc/neps/nep-0044-restructuring-numpy-docs.rst

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@@ -86,15 +86,17 @@ up-to-date official documentation that can be easily updated.
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Status and ideas of each type of doc content
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--------------------------------------------
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**Reference guide**
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Reference guide
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^^^^^^^^^^^^^^^
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NumPy has a quite complete reference guide. All functions are documented, most
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have examples, and most are cross-linked well with *See Also* sections. Further
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improving the reference guide is incremental work that can be done (and is being
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done) by many people. There are, however, many explanations in the reference
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guide. These can be moved to a more dedicated Explanations section on the docs.
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**How-to guides**
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How-to guides
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^^^^^^^^^^^^^
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NumPy does not have many how-to's. The subclassing and array ducktyping section
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may be an example of a how-to. Others that could be added are:
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- Performance (memory layout, profiling, use with Numba, Cython, or Pythran)
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- Writing generic code that works with NumPy, Dask, CuPy, pydata/sparse, etc.
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**Explanations**
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Explanations
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^^^^^^^^^^^^
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There is a reasonable amount of content on fundamental NumPy concepts such as
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indexing, vectorization, broadcasting, (g)ufuncs, and dtypes. This could be
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organized better and clarified to ensure it's really about explaining the concepts
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and not mixed with tutorial or how-to like content.
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There are few explanations about anything other than those fundamental NumPy
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concepts.
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concepts.
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Some examples of concepts that could be expanded:
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In addition, there are many explanations in the Reference Guide, which should be
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moved to this new dedicated Explanations section.
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**Tutorials**
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Tutorials
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^^^^^^^^^
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There's a lot of scope for writing better tutorials. We have a new *NumPy for
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absolute beginners tutorial* [3]_ (GSoD project of Anne Bonner). In addition we
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new high-quality content to the documentation.
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Data sets
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---------
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~~~~~~~~~
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Using interesting data in the NumPy docs requires giving all users access to
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that data, either inside NumPy or in a separate package. The former is not the
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best idea, since it's hard to do without increasing the size of NumPy
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significantly. Even for SciPy there has so far been no consensus on this (see
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`scipy PR 8707 <https://github.com/scipy/scipy/pull/8707>`_ on adding a new
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``scipy.datasets`` subpackage).
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So we'll aim for a new (pure Python) package, named ``numpy-datasets`` or
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``scipy-datasets`` or something similar. That package can take some lessons from
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how, e.g., scikit-learn ships data sets. Small data sets can be included in the
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repo, large data sets can be accessed via a downloader class or function.
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significantly.
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Whenever possible, documentation pages should use examples from the
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:mod:`scipy.datasets` package.
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Related work
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============

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